[1] M. D. Ekstrand, J. T. Riedl, and J. A. Konstan, ‘Collaborative filtering recommender systems’, Found. Trends® Human–Computer Interact., vol. 4, no. 2, pp. 81–173, 2011.
[2] G. Adomavicius and A. Tuzhilin, ‘Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions’, IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, 2005.
[3] P. Lops, M. De Gemmis, and G. Semeraro, ‘Content-based recommender systems: State of the art and trends’, in Recommender systems handbook, Springer, 2011, pp. 73–105.
[4] Z. Huang, H. Chen, and D. Zeng, ‘Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering’, ACM Trans. Inf. Syst., vol. 22, no. 1, pp. 116–142, 2004.
[5] X. Ma and R. Wang, ‘Personalized Scientific Paper Recommendation Based on Heterogeneous Graph Representation’, IEEE Access, vol. 7, pp. 79887–79894, 2019.
[6] L. Zhang, J. Xu, and C. Li, ‘A random-walk based recommendation algorithm considering item categories’, Neurocomputing, vol. 120, pp. 391–396, 2013.
[7] Z. Jiang, H. Liu, B. Fu, Z. Wu, and T. Zhang, ‘Recommendation in heterogeneous information networks based on generalized random walk model and bayesian personalized ranking’, in Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018, pp. 288–296.
[8] L. Page, S. Brin, R. Motwani, and T. Winograd, ‘The pagerank citation ranking: Bringing order to the web’, Stanford InfoLab, 1999.
[9] M. Gori, A. Pucci, V. Roma, and I. Siena, ‘Itemrank: A random-walk based scoring algorithm for recommender engines’, in IJCAI, 2007, vol. 7, pp. 2766–2771.
[10] P. Covington, J. Adams, and E. Sargin, ‘Deep neural networks for youtube recommendations’, in Proceedings of the 10th ACM conference on recommender systems, 2016, pp. 191–198.
[11] R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, ‘Graph convolutional neural networks for web-scale recommender systems’, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 974–983.
[12] S. K. Lam and J. Riedl, ‘Shilling recommender systems for fun and profit’, in Proceedings of the 13th international conference on World Wide Web, 2004, pp. 393–402.
[13] L. Chen, Y. Xu, F. Xie, M. Huang, and Z. Zheng, ‘Data poisoning attacks on neighborhood‐based recommender systems’, Trans. Emerg. Telecommun. Technol., p. e3872, 2019.
[14] I. Gunes, C. Kaleli, A. Bilge, and H. Polat, ‘Shilling attacks against recommender systems: a comprehensive survey’, Artif. Intell. Rev., vol. 42, no. 4, pp. 767–799, 2014.
[15] H. Zhang et al., ‘Data poisoning attack against knowledge graph embedding’, in Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019, pp. 4853–4859.
[16] G. Yang, N. Z. Gong, and Y. Cai, ‘Fake Co-visitation Injection Attacks to Recommender Systems’, in NDSS, 2017.
[17] R. Hu, Y. Guo, M. Pan, and Y. Gong, ‘Targeted poisoning attacks on social recommender systems’, in 2019 IEEE Global Communications Conference (GLOBECOM), 2019, pp. 1–6.
[18] M. Fang, G. Yang, N. Z. Gong, and J. Liu, ‘Poisoning attacks to graph-based recommender systems’, in Proceedings of the 34th Annual Computer Security Applications Conference, 2018, pp. 381–392.
[19] Z. Gyöngyi and H. Garcia-Molina, ‘Link spam alliances’, in Proceedings of the 31st international conference on Very large data bases, 2005, pp. 517–528.
[20] M. Gao, Z. Wu, and F. Jiang, ‘Userrank for item-based collaborative filtering recommendation’, Inf. Process. Lett., vol. 111, no. 9, pp. 440–446, 2011.
[21] B. Mobasher, R. Burke, R. Bhaumik, and C. Williams, ‘Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness’, ACM Trans. Internet Technol., vol. 7, no. 4, pp. 23-es, 2007.
[22] R. Burke, B. Mobasher, and R. Bhaumik, ‘Limited knowledge shilling attacks in collaborative filtering systems’, in Proceedings of 3rd International Workshop on Intelligent Techniques for Web Personalization (ITWP 2005), 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), 2005, pp. 17–24.
[23] M. P. O’Mahony, N. J. Hurley, and G. C. M. Silvestre, ‘Recommender systems: Attack types and strategies’, in AAAI, 2005, pp. 334–339.
[24] R. Burke, B. Mobasher, R. Bhaumik, and C. Williams, ‘Segment-based injection attacks against collaborative filtering recommender systems’, in Fifth IEEE International Conference on Data Mining (ICDM’05), 2005, p. 4 pp.
[25] F. Zhang, ‘Analysis of bandwagon and average hybrid attack model against trust-based recommender systems’, in 2011 Fifth International Conference on Management of e-Commerce and e-Government, 2011, pp. 269–273.
[26] C. Williams, B. Mobasher, R. Burke, J. Sandvig, and R. Bhaumik, ‘Detection of obfuscated attacks in collaborative recommender systems’, in Proceedings of the ECAI’06 Workshop on Recommender Systems, 2006, vol. 94.
[27] سیما ایرانمنش، محمدرضا زارع میرکآباد، فاطمه کاوه یزدی, "بررسی آسیبپذیری روشهای مبتنی بر گراف در سیستمهای توصیهگر با استفاده از روشهای مزرعه پیوند"، نخستین کنفرانس ملی محاسبات نرم, دانشگاه گیلان، 18 و 19 آبان 1394.
[28] S. Adalı, T. Liu, and M. Magdon-Ismail, ‘An analysis of optimal link bombs’, Theor. Comput. Sci., vol. 437, pp. 1–20, 2012.
[29] A. N. Nikolakopoulos, M. A. Kouneli, and J. D. Garofalakis, ‘Hierarchical itemspace rank: Exploiting hierarchy to alleviate sparsity in ranking-based recommendation’, Neurocomputing, vol. 163, pp. 126–136, 2015.
[30] F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens, ‘Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation’, IEEE Trans. Knowl. Data Eng., vol. 19, no. 3, pp. 355–369, 2007.
[31] D. Yang, J. He, H. Qin, Y. Xiao, and W. Wang, ‘A graph-based recommendation across heterogeneous domains’, in proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015, pp. 463–472.
[32] ‘MovieLens DataSet’, GroupLens Research. https://grouplens.org/datasets/movielens/.
[33] G. Guo, J. Zhang, and N. Yorke-Smith, ‘A Novel Bayesian Similarity Measure for Recommender Systems’, in Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013, pp. 2619–2625.
[34] G. Shani and A. Gunawardana, ‘Evaluating recommendation systems’, in Recommender systems handbook, Springer, 2011, pp. 257–297.
[35] M. Eirinaki, J. Gao, I. Varlamis, and K. Tserpes, ‘Recommender systems for large-scale social networks: A review of challenges and solutions’. Elsevier, 2018.